A Hybrid Algorithm for Computation of the Nonparametric Maximum Likelihood Estimator From Censored Data

作     者：Wellner, Jon A. Zhan, Yihui 

作者机构：Professor Department of Statistics University of Washington Seattle WA. Research Scientist Math-soft Statsci Division Seattle WA 

出 版 物：《Journal of the American Statistical Association》 

年 卷 期：1997年第92卷第439期

页      面：945-959页

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：Algorithm Censoring EM algorithm Hybrid method Iterative convex minorant Missing data Self consistency. 

摘      要：We present a hybrid algorithm for nonparametric maximum likelihood estimation from censured data when the log-likelihood is concave. The hybrid algorithm uses a composite algorithmic mapping combining the expectation-matimization (EM) algorithm and the (modified) iterative convex minorant (ICM) algorithm. Global convergence of the hybrid algorithm is proven; the iterates generated by the hybrid algorithm are shown to converge to the nonparametric maximum likelihood estimator (NPMLE) unambiguously. Numerical simulations demonstrate that the hybrid algorithm converges more rapidly than either of the EM or the naive ICM algorithm for doubly censored data. The speed of the hybrid algorithm makes it possible to accompany the NPMLE with bootstrap confidence bands. [ABSTRACT FROM AUTHOR]